Author Archives: selitskaya

Data Science Done Right (the Kitchen Style) #9

Picking up loose ends… When previously we were talking about the transformation matrix N-1 – the matrix of changing basis from the original to a basis of the element of the quotient space we project our dataset to – we … Continue reading

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Data Science Done Right (the Kitchen Style) #8

Using linear regression for dimensionality reduction In our linear model, designed and developed (in R) in the previous chapters, we calculate not only new images of the data, being projected on the chosen element of the quotient space that satisfy … Continue reading

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Data Science Done Right (the Kitchen Style) #7

We just created, in the previous post, a simple univariate, multivariable regression function ks_lm0 that calculates regression slopes and intercept. However, it would be interesting not only to find out what our quotient (or factor) space X/Y would be (which … Continue reading

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Data Science Done Right (the Kitchen Style) #6

Let’s implement univariate multivariable linear regression (see derivations in previous chapter) in R the way it is usually used, i.e. mapping our dataset A not just to an element of the Quotient space Yl, but, after that, mapping it back … Continue reading

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Data Science Done Right (the Kitchen Style) #5

A bit more formal on Linear Regression Let’s formalize a bit our Kitchen Style analogy from the previous post, into a more suitable for coding notation, still being a bit too verbose, as it is typical for Kitchen talks. We’ll … Continue reading

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Data Science Done Right (the Kitchen Style) #4

What a Linear Regression is? If we take a look at the general definition of the term Regression we will find something like: “transition to a simpler or less perfect state”. Perfection is quite a subjective category, and, depending on … Continue reading

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Data Science Done Right (the Kitchen Style) #3

More on the fundamental Method of Data Science When we want to model an unstructured collection of the real world phenomena we use such mathematical abstraction as Set. It can contain not just simple elements (or objects, or members – … Continue reading

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